
import polars as pl
from pathlib import Path

data_dir = Path('DATA')
data_file = ".."/ data_dir / "Electric_Vehicle_Population_Data.csv"

data = pl.read_csv(data_file)

#Breve visualizzazione dei dati
data.head()
data.describe()


data = data.select(pl.exclude(['VIN (1-10)','Postal Code','Legislative District','DOL Vehicle ID','Electric Utility','2020 Census Tract','Clean Alternative Fuel Vehicle (CAFV) Eligibility']))
data.describe()


data = data.drop_nulls()


data = data.with_columns(
    pl.col('Electric Vehicle Type').replace({
        'Plug-in Hybrid Electric Vehicle (PHEV)': 'PHEV',
        'Battery Electric Vehicle (BEV)': 'BEV'
    })
)


(
    data
    .group_by('Model Year')
    .agg(
        tot_per_year = pl.col('Model Year').count()
    )
    .sort(pl.col('Model Year'))
)


data = (
    data
    .filter(pl.col('Model Year')>2010)
)
# salvataggio dei dati nel file data.csv 
data.write_csv( ".."/ data_dir / "data.csv")


(
    data
    .group_by('Make')
    .agg(
        Vendita_per_marca = pl.col('Make').count()
    )
    .sort('Vendita_per_marca', descending = True)
)


import altair as alt

data_test = (
        data
        .group_by('Make')
        .agg(
            Vendita_per_marca = (pl.col('Make').count() / len(data)*100).round(3)
        )
        .filter(pl.col('Vendita_per_marca') < .5)
    )
    
base = (
    alt.Chart(data_test)
    .encode(
        x='Vendita_per_marca:Q',
        y=alt.Y('Make:N', sort='-x'),
        color=alt.Color('Vendita_per_marca:Q', scale=alt.Scale(scheme='oranges'))
    )
)

base.mark_bar()


(
    data
    .group_by('Make')
    .agg(
        Vendita_per_marca = (pl.col('Make').count() / len(data)*100).round(3)
    )
    .filter(pl.col('Vendita_per_marca') < .25)
    .sort('Vendita_per_marca', descending = True)
)


Total_County = (
    data
    .group_by('County')
    .agg(
        Total = pl.col('County').count()
    )
    .sort('Total', descending= True)
)

Total_County


num_car = len(data)

perc_county = (
    data
    .group_by('County')
    .agg(
        Total = (pl.col('County').count()/num_car*100).round(3)
    )
    .sort('Total', descending= True)
)

perc_county


import re
import pandas as pd

data_point = (
    data
    .select(pl.col('Vehicle Location'))
)

#Si nota che è di tipo str
print(f'Tipologia del dato :{data_point[0]}')

#Creiamo una lista contenente tuple  ('longitue','latitude'): (float, float)
coord_list = []
for row in data_point.rows():
    if row[0] is not None:
        numb = re.findall(r'-?\d+\.\d+|-?\d+', row[0])
        coord = (float(numb[0]), float(numb[1]))
        coord_list.append(coord)

#verifichiamo la lunghezza della lista
print(f'Lunghezza della lista coord_list: {len(coord_list)}')

#verifichiamo i primi 5 elementi di coord_list

print('Tipologia e dato di coord_list')
for i in range(5):
    print(f'Tipo: {type(coord_list[i])} Dato:{coord_list[i]}')

Tipologia del dato :shape: (1, 1)
┌─────────────────────────────┐
│ Vehicle Location            │
│ ---                         │
│ str                         │
╞═════════════════════════════╡
│ POINT (-122.36498 47.72238) │
└─────────────────────────────┘
Lunghezza della lista coord_list: 216683
Tipologia e dato di coord_list
Tipo: <class 'tuple'> Dato:(-122.36498, 47.72238)
Tipo: <class 'tuple'> Dato:(-122.30207, 47.64085)
Tipo: <class 'tuple'> Dato:(-122.54729, 47.42602)
Tipo: <class 'tuple'> Dato:(-122.89166, 47.03956)
Tipo: <class 'tuple'> Dato:(-122.87741, 47.05997)


(
    data
    .group_by('Make', 'Model Year')
    .agg(
        Vendita_per_marca = pl.col('Make').count()
    )
)


(
    data
    .group_by('Make', 'Electric Vehicle Type')
    .agg(
        Engine = pl.col('Electric Vehicle Type').count()
    )
)


(
    data
    .group_by('Make', 'Model', 'Model Year')
    .agg(
        Total = pl.col('Model').count()
    )
    .filter(pl.col('Total') >= 15)
    .sort([pl.col('Model Year'),pl.col('Total')], descending=False)
)


data.describe()


data_ER = (
    data
    .filter(pl.col('Electric Range') > 0)
)

data_ER.describe()


data_ER_EN = (
    data
    .select('Electric Vehicle Type', 'Electric Range')
    .filter(pl.col('Electric Range') > 0)
)

data_ER_EN = (
    data_ER_EN
    .group_by('Electric Vehicle Type', 'Electric Range')
    .agg(
        Count = pl.col('Electric Range').count()
    )
    .sort(pl.col('Electric Range'))
)

data_ER_EN = data_ER_EN.with_columns(
        pl.col('Electric Vehicle Type').replace({
            'Plug-in Hybrid Electric Vehicle (PHEV)': 'PHEV',
            'Battery Electric Vehicle (BEV)': 'BEV'
        })
    )


base = (
    alt.Chart(data_ER_EN)
    .encode(
        x = ('Electric Range:Q'),
        y = alt.Y('Count:Q'),
        color= alt.Color('Electric Vehicle Type:N', scale=alt.Scale(scheme='oranges'))
    )
)

base.mark_bar(cornerRadiusTopLeft=3, cornerRadiusTopRight=3)


data_range_prod = (
    data
    .select('Make', 'Electric Range', 'Electric Vehicle Type')
    .filter(pl.col('Electric Range') > 0 )
)
data_range_prod = data_range_prod.with_columns(
        pl.col('Electric Vehicle Type').replace({
            'Plug-in Hybrid Electric Vehicle (PHEV)': 'PHEV',
            'Battery Electric Vehicle (BEV)': 'BEV'
        })
    )
print(data_range_prod)

shape: (92_587, 3)
┌───────────┬────────────────┬───────────────────────┐
│ Make      ┆ Electric Range ┆ Electric Vehicle Type │
│ ---       ┆ ---            ┆ ---                   │
│ str       ┆ i64            ┆ str                   │
╞═══════════╪════════════════╪═══════════════════════╡
│ NISSAN    ┆ 75             ┆ BEV                   │
│ TESLA     ┆ 270            ┆ BEV                   │
│ TOYOTA    ┆ 25             ┆ PHEV                  │
│ FORD      ┆ 19             ┆ PHEV                  │
│ TESLA     ┆ 266            ┆ BEV                   │
│ …         ┆ …              ┆ …                     │
│ CHRYSLER  ┆ 32             ┆ PHEV                  │
│ CHEVROLET ┆ 259            ┆ BEV                   │
│ CHEVROLET ┆ 38             ┆ PHEV                  │
│ KIA       ┆ 33             ┆ PHEV                  │
│ CHEVROLET ┆ 38             ┆ PHEV                  │
└───────────┴────────────────┴───────────────────────┘


data_violin_graph=(
    data_range_prod
    .group_by('Make','Electric Range', )
    .agg(
        Count = pl.col('Electric Range').count()
    )
    .sort('Make')
)

print(data_violin_graph)

shape: (178, 3)
┌────────────┬────────────────┬───────┐
│ Make       ┆ Electric Range ┆ Count │
│ ---        ┆ ---            ┆ ---   │
│ str        ┆ i64            ┆ u32   │
╞════════════╪════════════════╪═══════╡
│ ALFA ROMEO ┆ 33             ┆ 90    │
│ AUDI       ┆ 16             ┆ 567   │
│ AUDI       ┆ 218            ┆ 73    │
│ AUDI       ┆ 19             ┆ 11    │
│ AUDI       ┆ 23             ┆ 510   │
│ …          ┆ …              ┆ …     │
│ VOLVO      ┆ 41             ┆ 162   │
│ VOLVO      ┆ 38             ┆ 8     │
│ VOLVO      ┆ 21             ┆ 30    │
│ VOLVO      ┆ 19             ┆ 123   │
│ VOLVO      ┆ 32             ┆ 608   │
└────────────┴────────────────┴───────┘


data_test_violin = (
    data_range_prod
    .group_by('Make')
    .agg(
        Count = pl.col('Make').count()/data_range_prod.height*100
    )
    .filter(pl.col('Count') < 5)
    .sort(pl.col('Count'))
)

base = (
    alt.Chart(data_test_violin)
    .encode(
        x='Count:Q',
        y=alt.Y('Make:N', sort='-x'),
        color=alt.Color('Count:Q', scale=alt.Scale(scheme='oranges'))
    )
)

base.mark_bar()

#print(data_test_violin)


make_list = (
    data_range_prod
    .group_by('Make')
    .agg(
        Count = pl.col('Make').count()/data_range_prod.height*100
    )
    .filter(pl.col('Count') > 0.5)
)


make_list = make_list['Make'].unique().sort().to_list()

make_list

['AUDI',
 'BMW',
 'CHEVROLET',
 'CHRYSLER',
 'DODGE',
 'FIAT',
 'FORD',
 'HONDA',
 'HYUNDAI',
 'JEEP',
 'KIA',
 'MAZDA',
 'MERCEDES-BENZ',
 'MITSUBISHI',
 'NISSAN',
 'PORSCHE',
 'TESLA',
 'TOYOTA',
 'VOLKSWAGEN',
 'VOLVO']


alt.data_transformers.disable_max_rows()

base = (
    alt.Chart(data_violin_graph)
    .encode(
        alt.X('Count:Q')
            .stack('center')
            .impute(None)
            .title(None)
            .axis(labels = False, values = [0], grid = False, ticks = True),
        alt.Y('Electric Range:Q'),    
        alt.Color('Make:N'),
        alt.Column('Make:N')
            .spacing(0)
            .header(titleOrient='bottom', labelOrient='bottom', labelPadding=0)
    )
    .configure_view(
        stroke = None
    )
)

base.mark_area(orient='horizontal')


bar = (
    alt.Chart(data_violin_graph)
    .mark_bar(cornerRadius=10, height=10)
    .encode(
        x = alt.X('min(Electric Range):Q').scale(domain=[-15, 350]).title('Electric Range'),
        x2 = 'max(Electric Range):Q',
        y = alt.Y('Make:N', sort = '-x').title(None),
    )
)

text_min = (
    alt.Chart(data_violin_graph)
    .mark_text(align='right', dx = -5)
    .encode(
        x = 'min(Electric Range):Q',
        y = alt.Y('Make:N'),
        text = 'min(Electric Range):Q'
    )
)

text_max = (
    alt.Chart(data_violin_graph)
    .mark_text(align='left', dx = 5)
    .encode(
        x = 'max(Electric Range):Q',
        y = alt.Y('Make:N'),
        text = 'max(Electric Range):Q'
    )
)

(bar + text_min + text_max).properties(
    title = alt.Title(text = 'Electric Range')
)


filtered_data = (
    data_violin_graph
    .group_by("Make")
    .agg([
        pl.col("Electric Range").max().alias("max_Electric_Range"),
        pl.col("Electric Range").min().alias("min_Electric_Range"),
    ])
    .filter((pl.col("max_Electric_Range") - pl.col("min_Electric_Range")) < 5)
)

filter_list = filtered_data['Make'].unique().sort().to_list()

filter_list

data_violin_graph = (
    data_violin_graph
    .filter(pl.col('Make').is_in(list(set(make_list) - set(filter_list))))
)

bar = (
    alt.Chart(data_violin_graph)
    .mark_bar(cornerRadius=10, height=10)
    .encode(
        x = alt.X('min(Electric Range):Q').scale(domain=[-15, 350]).title('Electric Range'),
        x2 = 'max(Electric Range):Q',
        y = alt.Y('Make:N', sort = '-x').title(None),
        color=alt.value('orange')
    )
)

text_min = (
    alt.Chart(data_violin_graph)
    .mark_text(align='right', dx = -5)
    .encode(
        x = 'min(Electric Range):Q',
        y = alt.Y('Make:N'),
        text = 'min(Electric Range):Q'
    )
)

text_max = (
    alt.Chart(data_violin_graph)
    .mark_text(align='left', dx = 5)
    .encode(
        x = 'max(Electric Range):Q',
        y = alt.Y('Make:N'),
        text = 'max(Electric Range):Q'
    )
)

(bar + text_min + text_max).properties(
    title = alt.Title(text = 'Electric Range')
)


#Numero dati con cui eseguiamo l'analisi
data_label = (
    data
    .select('Electric Vehicle Type', 'Electric Range')
    .filter(pl.col('Electric Range') > 0)
)

data_label = data_label.with_columns(
        pl.col('Electric Vehicle Type').replace({
            'Plug-in Hybrid Electric Vehicle (PHEV)': 'PHEV',
            'Battery Electric Vehicle (BEV)': 'BEV'
        })
    )

data_label.height
data_label

data_mean = (
    data_label
    .mean()
    .select('Electric Range').item()
)

round(data_mean, 2)

115.59


data_label


data_mean_by_engine = (
    data_label
    .group_by('Electric Vehicle Type')
    .agg(
        Count = pl.col('Electric Range').count(),
        Mean = pl.col('Electric Range').mean()
    )
    .with_columns(
        pl.col('Mean').round(2).alias('Mean')  
    )
)

data_mean_by_engine

value = data_mean_by_engine.row(0)[1]

print(data_mean_by_engine)

shape: (2, 3)
┌───────────────────────┬───────┬────────┐
│ Electric Vehicle Type ┆ Count ┆ Mean   │
│ ---                   ┆ ---   ┆ ---    │
│ str                   ┆ u32   ┆ f64    │
╞═══════════════════════╪═══════╪════════╡
│ PHEV                  ┆ 45561 ┆ 31.03  │
│ BEV                   ┆ 47026 ┆ 197.52 │
└───────────────────────┴───────┴────────┘


data_strip_plot = (
    data
    .select('Make', 'Electric Range', 'Electric Vehicle Type')
    .filter(pl.col('Electric Range') > 0 )
)
data_strip_plot = data_strip_plot.with_columns(
    pl.col('Electric Vehicle Type').replace({
        'Plug-in Hybrid Electric Vehicle (PHEV)': 'PHEV',
        'Battery Electric Vehicle (BEV)': 'BEV'
    })
)

gaussian_jitter = alt.Chart(data_strip_plot).mark_circle(size = 8).encode(
    y = 'Electric Vehicle Type:N',
    x = 'Electric Range:Q',
    yOffset='jitter:Q',
    color=alt.Color('Electric Vehicle Type:N', scale=alt.Scale(scheme='oranges')).legend(None)
).transform_calculate(
    jitter = "sqrt(-2*log(random()))*cos(2*PI*random())"
)

uniform_jitter = gaussian_jitter.transform_calculate(
    jitter='random()'
).encode(
    alt.Y('Electric Vehicle Type:N').axis(None)
)

(gaussian_jitter | uniform_jitter).resolve_scale(yOffset='independent')


def make_strip_plot(data):
    data_range_prod = (
        data
        .select('Make', 'Electric Range', 'Electric Vehicle Type')
        .filter(pl.col('Electric Range') > 0 )
    )
    data_range_prod = data_range_prod.with_columns(
        pl.col('Electric Vehicle Type').replace({
            'Plug-in Hybrid Electric Vehicle (PHEV)': 'PHEV',
            'Battery Electric Vehicle (BEV)': 'BEV'
        })
    )

    range_graph = (
        data_range_prod
        .group_by('Make','Electric Range', )
        .agg(
            Count = pl.col('Electric Range').count()
        )
        .sort('Make')
    )

    make_list = (
        data_range_prod
        .group_by('Make')
        .agg(
            Count = pl.col('Make').count()/data_range_prod.height*100
        )
        .filter(pl.col('Count') > 0.5)
    )

    make_list = make_list['Make'].unique().sort().to_list()
    
    filtered_data = (
        range_graph
        .group_by("Make")
        .agg([
            pl.col("Electric Range").max().alias("Max Range"),
            pl.col("Electric Range").min().alias("Min Range"),
        ])
    )

    filtered_data = (
        filtered_data
        .filter((pl.col("Max Range") - pl.col("Min Range")) < 5)
    )

    filter_list = filtered_data['Make'].unique().sort().to_list()
    
    

    return list(set(make_list) - set(filter_list))

test = make_strip_plot(data)

data_jitter_plot = (
    data
    .select('Make', 'Electric Range', )
    .filter(pl.col('Electric Range') > 0)
    .filter(pl.col('Make').is_in(test))
)

gaussian_jitter = alt.Chart(data_jitter_plot).mark_circle(size = 8).encode(
    y = 'Make:N',
    x = 'Electric Range:Q',
    yOffset='jitter:Q',
    color=alt.Color('Make:N', scale=alt.Scale(scheme='oranges')).legend(None)
).transform_calculate(
    jitter = "sqrt(-2*log(random()))*cos(2*PI*random())"
)

(gaussian_jitter).resolve_scale(yOffset='independent')


test_list = ['TESLA', 'HYUNDAI', 'KIA', 'TOYOTA']

test_grafico = (
    data
    .select('Make', 'Electric Range', 'Electric Vehicle Type')
    .filter(pl.col('Make').is_in(test_list))
    .filter(pl.col('Electric Range') > 0)
)

gaussian_jitter = alt.Chart(test_grafico).mark_circle(size = 8).encode(
    y = 'Make:N',
    x = 'Electric Range:Q',
    yOffset='jitter:Q',
    color=alt.Color('Electric Vehicle Type:N', scale=alt.Scale(scheme='purpleorange'))
).transform_calculate(
    jitter = "sqrt(-2*log(random()))*cos(2*PI*random())"
)

(gaussian_jitter).resolve_scale(yOffset='independent')


data_prezzi = (
    data.filter(pl.col('Base MSRP') > 0)
)

data_prezzi.describe()


data_year_price = (
    data_prezzi
    .group_by('Model Year')
    .agg(
        Mean = pl.col('Base MSRP').mean(),
        Count = pl.col('Model Year').count()
    )
    .filter(pl.col('Count')>100)
    .sort('Model Year')
)

data_year_price


alt.Chart(data_year_price).mark_line(point={'color': 'orange', 'size': 30}, color='orange').encode(
    x='Model Year:O',
    y='Mean:Q',
).properties(
    width = 500
)


data_scatter_price_range = (
    data_prezzi
    .select('Base MSRP', 'Electric Range', 'Electric Vehicle Type')
    .filter(pl.col('Electric Range') > 0)
    .filter(pl.col('Base MSRP') < 120000)
)
data_scatter_price_range


alt.Chart(data_scatter_price_range).mark_point().encode(
    x='Electric Range:Q',
    y='Base MSRP:Q',
    color=alt.Color('Electric Vehicle Type:N', scale=alt.Scale(scheme='purpleorange')),
    size= 'count()'
)


data_HONDA = (
    data
    .filter(
        pl.col('Make') == 'HONDA'
    )
)


test = (
    data_HONDA
    #.select('Make')
    .group_by('Electric Vehicle Type')
    .agg(
        Tot_Engine = ((pl.col('Electric Vehicle Type').count())/data_HONDA.height).round(2)
    )
    .sort(pl.col('Electric Vehicle Type'))
)

test


model_engine =(
    data_HONDA
    .select('Model', 'Electric Vehicle Type').unique()
)

(
    model_engine
    .group_by('Electric Vehicle Type')
    .agg(
        tot = pl.col('Electric Vehicle Type').count()
    )
    .sort(pl.col('Electric Vehicle Type'))
)


data_mean_price = (
    data
    .filter(pl.col('Base MSRP') > 0)
    .select(pl.col('Base MSRP').mean().round(2))
)

print(data_mean_price.row(0)[0])

data_mean_price_engine = (
    data
    .filter(pl.col('Base MSRP') > 0)
    .group_by('Electric Vehicle Type')
    .agg(
        Mean = pl.col('Base MSRP').mean().round(2)
    )
    .sort(pl.col('Electric Vehicle Type'))
)

data_mean_price_engine

56488.98

statistic	VIN (1-10)	County	City	State	Postal Code	Model Year	Make	Model	Electric Vehicle Type	Clean Alternative Fuel Vehicle (CAFV) Eligibility	Electric Range	Base MSRP	Legislative District	DOL Vehicle ID	Vehicle Location	Electric Utility	2020 Census Tract
str	str	str	str	str	f64	f64	str	str	str	str	f64	f64	f64	f64	str	str	f64
"count"	"216772"	"216767"	"216767"	"216772"	216767.0	216772.0	"216772"	"216772"	"216772"	"216772"	216753.0	216753.0	216321.0	216772.0	"216761"	"216767"	216767.0
"null_count"	"0"	"5"	"5"	"0"	5.0	0.0	"0"	"0"	"0"	"0"	19.0	19.0	451.0	0.0	"11"	"5"	5.0
"mean"	null	null	null	null	98179.750714	2021.129039	null	null	null	null	49.428386	870.987045	28.920114	2.3045e8	null	null	5.2982e10
"std"	null	null	null	null	2458.320323	2.983918	null	null	null	null	86.224511	7544.671592	14.907934	7.0450e7	null	null	1.5147e9
"min"	"1C4JJXN60P"	"Ada"	"Aberdeen"	"AE"	1731.0	1999.0	"ACURA"	"330E"	"Battery Electric Vehicle (BEV)"	"Clean Alternative Fuel Vehicle…	0.0	0.0	1.0	4385.0	"POINT (-100.50078 31.4168)"	"AVISTA CORP"	1.0010e9
"25%"	null	null	null	null	98052.0	2020.0	null	null	null	null	0.0	0.0	17.0	1.96232816e8	null	null	5.3033e10
"50%"	null	null	null	null	98125.0	2022.0	null	null	null	null	0.0	0.0	32.0	2.44032309e8	null	null	5.3033e10
"75%"	null	null	null	null	98374.0	2023.0	null	null	null	null	42.0	0.0	42.0	2.64906967e8	null	null	5.3053e10
"max"	"ZHWUC1ZM1R"	"Yuba"	"Zillah"	"WY"	99577.0	2025.0	"WHEEGO ELECTRIC CARS"	"ZDX"	"Plug-in Hybrid Electric Vehicl…	"Not eligible due to low batter…	337.0	845000.0	49.0	4.79254772e8	"POINT (-98.72277 29.44539)"	"PUGET SOUND ENERGY INC\|\|PUD NO…	5.6021e10

statistic	County	City	State	Model Year	Make	Model	Electric Vehicle Type	Electric Range	Base MSRP	Vehicle Location
str	str	str	str	f64	str	str	str	f64	f64	str
"count"	"216767"	"216767"	"216772"	216772.0	"216772"	"216772"	"216772"	216753.0	216753.0	"216761"
"null_count"	"5"	"5"	"0"	0.0	"0"	"0"	"0"	19.0	19.0	"11"
"mean"	null	null	null	2021.129039	null	null	null	49.428386	870.987045	null
"std"	null	null	null	2.983918	null	null	null	86.224511	7544.671592	null
"min"	"Ada"	"Aberdeen"	"AE"	1999.0	"ACURA"	"330E"	"Battery Electric Vehicle (BEV)"	0.0	0.0	"POINT (-100.50078 31.4168)"
"25%"	null	null	null	2020.0	null	null	null	0.0	0.0	null
"50%"	null	null	null	2022.0	null	null	null	0.0	0.0	null
"75%"	null	null	null	2023.0	null	null	null	42.0	0.0	null
"max"	"Yuba"	"Zillah"	"WY"	2025.0	"WHEEGO ELECTRIC CARS"	"ZDX"	"Plug-in Hybrid Electric Vehicl…	337.0	845000.0	"POINT (-98.72277 29.44539)"

Model Year	tot_per_year
i64	u32
1999	2
2000	7
2002	2
2003	1
2008	23
…	…
2021	20074
2022	28592
2023	60292
2024	40102
2025	1428

Make	Vendita_per_marca
str	u32
"TESLA"	93883
"CHEVROLET"	15862
"NISSAN"	15011
"FORD"	11477
"KIA"	10089
…	…
"TH!NK"	5
"AZURE DYNAMICS"	4
"ROLLS-ROYCE"	3
"RAM"	2
"VINFAST"	1

Make	Vendita_per_marca
str	f64
"LINCOLN"	0.148
"LUCID"	0.146
"GENESIS"	0.137
"SMART"	0.113
"JAGUAR"	0.109
…	…
"AZURE DYNAMICS"	0.002
"TH!NK"	0.002
"RAM"	0.001
"ROLLS-ROYCE"	0.001
"VINFAST"	0.0

Estrazione dei dati.¶

Manipolazione dei dati¶

Lista colonne da eliminare¶

Lista colonne che verranno utilizzate con la relativa descrizione¶

Obiettivi dell'analisi¶

Vendita annuale di auto BEV/PHEV¶

Vendita di auto per marca¶

Vendita di auto per contea¶

Vendita di auto in base alla localizzazione¶

Vendita di auto in base a produttore e anno¶

Vendita di auto in base a tipologia di motore e produttore¶

Ventita annuale di auto in base al modello e marca¶

Analisi su prestazioni e costi delle auto¶

Analisi Electric Vehicle Type¶

Distribuzione Electric Range per tipologia di motore¶

Autonomia in elettrico rispetto al produttore¶

Etichette utili all'analisi¶

Strip plot jitter¶

ANALISI SUI PREZZI¶

PARTE DI MIGLIORAMENTO¶

County	Total
str	u32
"King"	110122
"Snohomish"	26195
"Pierce"	17392
"Clark"	12937
"Thurston"	7936
…	…
"Brevard"	1
"Salt Lake"	1
"James City"	1
"Wasco"	1
"Currituck"	1

County	Total
str	f64
"King"	50.822
"Snohomish"	12.089
"Pierce"	8.026
"Clark"	5.97
"Thurston"	3.662
…	…
"Penobscot"	0.0
"New York"	0.0
"Lane"	0.0
"Lee"	0.0
"Denton"	0.0

Make	Model Year	Vendita_per_marca
str	i64	u32
"JAGUAR"	2019	117
"CHRYSLER"	2022	422
"AUDI"	2016	211
"CHRYSLER"	2023	1289
"GENESIS"	2024	112
…	…	…
"SMART"	2014	64
"TOYOTA"	2015	106
"MAZDA"	2025	72
"TESLA"	2013	723
"HYUNDAI"	2021	319

Make	Electric Vehicle Type	Engine
str	str	u32
"LUCID"	"BEV"	317
"JAGUAR"	"BEV"	237
"AUDI"	"PHEV"	1709
"GENESIS"	"BEV"	297
"DODGE"	"PHEV"	701
…	…	…
"KIA"	"BEV"	7198
"KIA"	"PHEV"	2891
"CHEVROLET"	"BEV"	11053
"TH!NK"	"BEV"	5
"ACURA"	"BEV"	100

Make	Model	Model Year	Total
str	str	i64	u32
"CHEVROLET"	"VOLT"	2011	73
"NISSAN"	"LEAF"	2011	610
"MITSUBISHI"	"I-MIEV"	2012	40
"TESLA"	"MODEL S"	2012	128
"TOYOTA"	"PRIUS PLUG-IN"	2012	368
…	…	…	…
"VOLVO"	"XC60"	2025	95
"BMW"	"IX"	2025	112
"NISSAN"	"LEAF"	2025	139
"BMW"	"X5"	2025	189
"RIVIAN"	"R1S"	2025	412

statistic	County	City	State	Model Year	Make	Model	Electric Vehicle Type	Electric Range	Base MSRP	Vehicle Location
str	str	str	str	f64	str	str	str	f64	f64	str
"count"	"216683"	"216683"	"216683"	216683.0	"216683"	"216683"	"216683"	216683.0	216683.0	"216683"
"null_count"	"0"	"0"	"0"	0.0	"0"	"0"	"0"	0.0	0.0	"0"
"mean"	null	null	null	2021.132636	null	null	null	49.391009	849.617044	null
"std"	null	null	null	2.974965	null	null	null	86.198317	7399.818083	null
"min"	"Ada"	"Aberdeen"	"AK"	2011.0	"ACURA"	"330E"	"BEV"	0.0	0.0	"POINT (-100.50078 31.4168)"
"25%"	null	null	null	2020.0	null	null	null	0.0	0.0	null
"50%"	null	null	null	2022.0	null	null	null	0.0	0.0	null
"75%"	null	null	null	2023.0	null	null	null	42.0	0.0	null
"max"	"Yuba"	"Zillah"	"WY"	2025.0	"VOLVO"	"ZDX"	"PHEV"	337.0	845000.0	"POINT (-98.72277 29.44539)"

statistic	County	City	State	Model Year	Make	Model	Electric Vehicle Type	Electric Range	Base MSRP	Vehicle Location
str	str	str	str	f64	str	str	str	f64	f64	str
"count"	"92587"	"92587"	"92587"	92587.0	"92587"	"92587"	"92587"	92587.0	92587.0	"92587"
"null_count"	"0"	"0"	"0"	0.0	"0"	"0"	"0"	0.0	0.0	"0"
"mean"	null	null	null	2018.869453	null	null	null	115.590655	1988.373854	null
"std"	null	null	null	3.240357	null	null	null	98.67561	11219.89275	null
"min"	"Adams"	"Aberdeen"	"AL"	2011.0	"ALFA ROMEO"	"330E"	"BEV"	6.0	0.0	"POINT (-100.50078 31.4168)"
"25%"	null	null	null	2017.0	null	null	null	30.0	0.0	null
"50%"	null	null	null	2019.0	null	null	null	73.0	0.0	null
"75%"	null	null	null	2021.0	null	null	null	215.0	0.0	null
"max"	"Yakima"	"Zillah"	"WI"	2025.0	"VOLVO"	"XM"	"PHEV"	337.0	845000.0	"POINT (-98.52212 29.61445)"

statistic	County	City	State	Model Year	Make	Model	Electric Vehicle Type	Electric Range	Base MSRP	Vehicle Location
str	str	str	str	f64	str	str	str	f64	f64	str
"count"	"3259"	"3259"	"3259"	3259.0	"3259"	"3259"	"3259"	3259.0	3259.0	"3259"
"null_count"	"0"	"0"	"0"	0.0	"0"	"0"	"0"	0.0	0.0	"0"
"mean"	null	null	null	2015.717091	null	null	null	119.622277	56488.975146	null
"std"	null	null	null	2.39341	null	null	null	89.979303	22311.569047	null
"min"	"Adams"	"Aberdeen"	"CA"	2011.0	"BMW"	"330E"	"BEV"	12.0	31950.0	"POINT (-115.20278 36.29143)"
"25%"	null	null	null	2013.0	null	null	null	17.0	39995.0	null
"50%"	null	null	null	2016.0	null	null	null	93.0	59900.0	null
"75%"	null	null	null	2018.0	null	null	null	208.0	69900.0	null
"max"	"Yakima"	"Zillah"	"WA"	2020.0	"VOLVO"	"XC90"	"PHEV"	265.0	845000.0	"POINT (-97.85322 30.57955)"

Model Year	Mean	Count
i64	f64	u32
2012	63508.571429	140
2013	69900.0	723
2014	69900.0	617
2016	32203.369272	371
2017	39228.54	250
2018	53920.394127	647
2019	44561.356994	479

Base MSRP	Electric Range	Electric Vehicle Type
i64	i64	str
44100	14	"PHEV"
31950	93	"BEV"
69900	208	"BEV"
31950	93	"BEV"
59900	265	"BEV"
…	…	…
69900	208	"BEV"
69900	208	"BEV"
45600	14	"PHEV"
109000	245	"BEV"
39995	32	"PHEV"